Effect of Ultrasound‐Stimulated Microbubbles and Hyperthermia on Tumor Vasculature of Breast Cancer Xenograft
Bibliographic record
Abstract
OBJECTIVE: The objective of the present study was to investigate the treatment effects of ultrasound-stimulated microbubbles (USMB) and hyperthermia (HT) on breast tumor vasculature. METHODS: Tumor-bearing mice with breast cancer xenografts (MDA-MB-231), were exposed to different treatment conditions consisting of control (no treatment), USMB alone, HT alone, USMB with HT exposures of 10 and 50 minutes. Quantitative 3D Doppler ultrasound and photoacoustic imaging were used to detect tumor blood flow and oxygen saturation, respectively. In addition, histopathological analysis including TUNEL staining for cell death, and CD31 staining for the vessel count, was performed to complement the results of power Doppler and photoacoustic imaging. RESULTS: Results demonstrated a decrease in tumor blood flow as well as oxygenation level following 50 minutes HT treatment either alone or combined with USMB. In contrast, 10 minutes HT alone or combined with USMB had minimal effects on blood flow and tumor oxygenation level. Treatment with HT for 50 minutes caused drops in tumor oxygenation, which were not evident with USMB treatment alone. Additionally, results revealed an increase in cell death after 10 minutes HT with or without USMB and a decrease in vessel count compared to control. Unlike previous studies which demonstrated synergistic treatment effects combining USMB with other modalities such as radiation or chemotherapy, USMB and HT effects were not synergistic in the present study. CONCLUSION: The results here demonstrated HT and USMB both alone or together resulted in a significant reduction in tumor blood flow, tumor oxygenation, and vessel count with observed increases in cell death response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".